{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Navigation\n",
    "\n",
    "---\n",
    "\n",
    "You are welcome to use this coding environment to train your agent for the project.  Follow the instructions below to get started!\n",
    "\n",
    "### 1. Start the Environment\n",
    "\n",
    "Run the next code cell to install a few packages.  This line will take a few minutes to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip -q install ./python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The environment is already saved in the Workspace and can be accessed at the file path provided below.  Please run the next code cell without making any changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:unityagents:\n",
      "'Academy' started successfully!\n",
      "Unity Academy name: Academy\n",
      "        Number of Brains: 1\n",
      "        Number of External Brains : 1\n",
      "        Lesson number : 0\n",
      "        Reset Parameters :\n",
      "\t\t\n",
      "Unity brain name: BananaBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 37\n",
      "        Number of stacked Vector Observation: 1\n",
      "        Vector Action space type: discrete\n",
      "        Vector Action space size (per agent): 4\n",
      "        Vector Action descriptions: , , , \n"
     ]
    }
   ],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "\n",
    "# please do not modify the line below\n",
    "env = UnityEnvironment(file_name=\"/data/Banana_Linux_NoVis/Banana.x86_64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "Run the code cell below to print some information about the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 1\n",
      "Number of actions: 4\n",
      "States look like: [ 1.          0.          0.          0.          0.84408134  0.          0.\n",
      "  1.          0.          0.0748472   0.          1.          0.          0.\n",
      "  0.25755     1.          0.          0.          0.          0.74177343\n",
      "  0.          1.          0.          0.          0.25854847  0.          0.\n",
      "  1.          0.          0.09355672  0.          1.          0.          0.\n",
      "  0.31969345  0.          0.        ]\n",
      "States have length: 37\n"
     ]
    }
   ],
   "source": [
    "# reset the environment\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "\n",
    "# number of agents in the environment\n",
    "print('Number of agents:', len(env_info.agents))\n",
    "\n",
    "# number of actions\n",
    "action_size = brain.vector_action_space_size\n",
    "print('Number of actions:', action_size)\n",
    "\n",
    "# examine the state space \n",
    "state = env_info.vector_observations[0]\n",
    "print('States look like:', state)\n",
    "state_size = len(state)\n",
    "print('States have length:', state_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Take Random Actions in the Environment\n",
    "\n",
    "In the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.\n",
    "\n",
    "Note that **in this coding environment, you will not be able to watch the agent while it is training**, and you should set `train_mode=True` to restart the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#env_info = env.reset(train_mode=True)[brain_name] # reset the environment\n",
    "#state = env_info.vector_observations[0]            # get the current state\n",
    "#score = 0                                          # initialize the score\n",
    "#while True:\n",
    "#    action = np.random.randint(action_size)        # select an action\n",
    "#    env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "#    next_state = env_info.vector_observations[0]   # get the next state\n",
    "#    reward = env_info.rewards[0]                   # get the reward\n",
    "#    done = env_info.local_done[0]                  # see if episode has finished\n",
    "#    score += reward                                # update the score\n",
    "#    state = next_state                             # roll over the state to next time step\n",
    "#    if done:                                       # exit loop if episode finished\n",
    "#        break\n",
    "    \n",
    "#print(\"Score: {}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. It's Your Turn!\n",
    "\n",
    "Now it's your turn to train your own agent to solve the environment!  A few **important notes**:\n",
    "- When training the environment, set `train_mode=True`, so that the line for resetting the environment looks like the following:\n",
    "```python\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "```\n",
    "- To structure your work, you're welcome to work directly in this Jupyter notebook, or you might like to start over with a new file!  You can see the list of files in the workspace by clicking on **_Jupyter_** in the top left corner of the notebook.\n",
    "- In this coding environment, you will not be able to watch the agent while it is training.  However, **_after training the agent_**, you can download the saved model weights to watch the agent on your own machine! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Deep-Q-Network procedure **dqn** performs the double loop. External loop (by episodes) is executed till \n",
    "the number of episodes reached the maximal number n_episodes = 2000 or the completion criteria performed. \n",
    "The environment env is reset with the paarmeter train_mode=True. For the completion criteria, we check\n",
    "\n",
    "     np.mean(scores_window) >=13,  \n",
    "    \n",
    "where scores_window is the array of the type deque realizing the shifting window of length <= 100. \n",
    "The element scores_window[i] contains the score achieved by the algorithm on the episode i.\n",
    "\n",
    "In the internal loop, **dqn** gets the current action from the **agent**. By this _action_ **dqn** gets \n",
    "_state_ and _reward_ from the Unity environment _env_. Then, the agent accepts params state, action, reward ,next_state, done\n",
    "to the next training step. The variable score accumulates obtained rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import math\n",
    "import datetime\n",
    "import torch                       \n",
    "from dqn_agent import Agent\n",
    "from collections import deque\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "def dqn(n_episodes=2000, eps_start=.99, eps_end=0.01, eps_decay = .996, train_numb = 0):\n",
    "    \"\"\"Deep Q-Learning.\n",
    "    \n",
    "    Params\n",
    "    ======\n",
    "        n_episodes (int): maximum number of training episodes\n",
    "        eps_start (float): starting value of epsilon, for epsilon-greedy action selection\n",
    "        eps_end (float): minimum value of epsilon\n",
    "        eps_decay (float): multiplicative factor (per episode) for decreasing epsilon\n",
    "    \"\"\"\n",
    "    start_time = time.time()                               # start time \n",
    "    scores = []                                            # list of scores for each episode\n",
    "    scores_window = deque(maxlen=100)                      # last 100 scores\n",
    "    eps = eps_start                                        # initialize epsilon\n",
    "\n",
    "    for i_episode in range(1, n_episodes+1):               # loop by episodes\n",
    "        env_info = env.reset(train_mode = True)[brain_name]\n",
    "        state = env_info.vector_observations[0]            # get the current state \n",
    "        score = 0                                          # reset the score counter\n",
    "        done = False                                       # are we done yet?\n",
    "        while not done:                                    # internal loop in the episode\n",
    "            action = agent.act(state,eps)                  # next action from the agent \n",
    "            action = int(action)                           # cast to int\n",
    "            env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "            next_state = env_info.vector_observations[0]   # get the next state\n",
    "            reward = env_info.rewards[0]                   # get the reward\n",
    "            done = env_info.local_done[0]                  # done is true if episode has finished\n",
    "            agent.step(state,action,reward,next_state, done) # next learning step by state and reward\n",
    "            score += reward                                # count our rewards\n",
    "            state = next_state                             # update state\n",
    "            if done:                                       # done ?\n",
    "                break                                      # save score\n",
    "        scores_window.append(score)                        # save score in the deque with 100 or less elements\n",
    "        scores.append(score)\n",
    "        \n",
    "        eps = max(eps_end,eps_decay*eps)                   # make epsilon a bit smaller\n",
    "        \n",
    "        count = 0                                          # how many times we've reached 13\n",
    "        for j in range(len(scores_window)):                \n",
    "            if scores_window[j] >= 13:\n",
    "                count+=1\n",
    "                \n",
    "        elapsed = datetime.timedelta(seconds = time.time()-start_time)  # elapsed time\n",
    "        \n",
    "        print('\\rEpisode: {}, elapsed: {}, Avg.Score: {:.2f},  score {}, How many scores >= 13: {}, eps.: {:.2f}'. \\\n",
    "            format(i_episode, elapsed, np.mean(scores_window), score, count, eps), end=\"\")\n",
    "        \n",
    "        if np.mean(scores_window) >=13:  # check completion criteria.\n",
    "            print(\"\\n terminating at episode :\", i_episode, \"ave reward reached +13 over 100 episodes\")\n",
    "            break\n",
    "            \n",
    "    torch.save(agent.qnetwork_local.state_dict(), 'weights_'+str(train_numb)+'.trn') # save the weights into the file \n",
    "    return scores, i_episode\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Training sessions with different hyperparameters\n",
    "\n",
    "We run the number of training sessions (in according to the parameter numb_of_trains) with different parameters \n",
    "fc1_units, fc2_units, eps_start. For each session, in the function **dqn**, we save obtained weights by the function \n",
    "_torch.save_ of the package **PyTorch**:\n",
    "\n",
    "    torch.save(agent.qnetwork_local.state_dict(), 'weights_'+str(train_numb)+'.trn') \n",
    "    \n",
    "For each training session, the obtained weights are saved into the file 'weights_'+str(train_numb)+'.trn'. We get files: weights_0.trn, weights_1.trn, weights_2.trn, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fc1_units:  96 , fc2_units:  88\n",
      "train_numb:  0 eps_start:  0.99\n",
      "Episode: 585, elapsed: 0:09:51.835191, Avg.Score: 13.02,  score 16.0, How many scores >= 13: 58, eps.: 0.09\n",
      " terminating at episode : 585 ave reward reached +13 over 100 episodes\n"
     ]
    },
    {
     "data": {
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+gt9sw7D4ycU1DywFgIKFXQwu6jxXHdkcx4//tR4A8NiyHWi687LA5/qff75nPRYRpxA1kYvuhe+izIx5DhqmMjnc/sQa3Pfp6bjrufX49cubsfq7F2nbqqfi3LlNROzdmZyVbhiE7/3zPbR0dGPyiDoA+V8vz7+7B/PfMQT8yRU78cmZ+b/pS+vz+f7RKNMKs2DBe3uQ9fkFobsx6DJlegKyYoiKo+TC3sNejNdMT6/2xSIEPUyk63dq3UIbKqlM1koN9CrSpV+JSG/FAPlyukEQN4GOlLGPGERNKyk9bpcSi0Q8883T2ZzvwKxq5QC9Z8WQsBMVRzJE5BaEEjs7rsf3Wz5NUGqrKZyw+7cNcjhVQIMeR7Vi5NIJHZqSAW6IgUuxbJ+40TiFXX8x0YgzK0bdL+XzOdT9Qgv6q61YSNiJiiNV4oi9p6ee8oCDbfn2pT1/GGH3a6pb81SFc2hXHgrSL9Xm2deerwekqwXjRlxZNCMfsfunWAJGuQM1TVQmm4NtPVTtMTQ3ht6YdQqQsBMViG4dymLo6Tx2cfSgFkuQKf9hCCfs/h6776pE4L4LXAD6G516bDliL0TYM5YdZXxm1BuOd8TuLo/ZXM73c6gT8ThZMQShJ+kTKYWlx60YK00uYPsChd1tUDPLubWYhR++Hjv8bzyc53PovQhixcgeexgrRo22hRWjvg9uf5NoxD0rBjAidr+xHl3ErpuN2hOQsBMVR+UNnor/Aw6eFnincRPcTI7joCYdT9DWlQ5lF/n1j3P/qfyA/v1Qd2vpKMyKUUXVzYrpdgkSohGmnWBk9ZNz34hdJ+xe9k4pIWEnKg6/QSsAaJw7H7e5lHpVCSqjjXPn489vbvdtt+NAJxrnzrfS6oSAPfH2TjTOnW8N6LnhpZvn3rVIu/3u59dj0ree1R8vx9Haqa9d/681uzH1f57HzxZs8D03ECwrhoOjO5Nv84c3mtA4dz6eXLEDjXPno3HufKzdfVCfFaPZJuqXL9kSfDkI9TAiUletmOfe3QMd/hE79/XYdRkwNHhKEC74TRMXPLLUX4SBcNbHn5fqJ+7IbDLLuT76lv78fguEeAmnWoJX8IBZF1xHJset2a8q2/cbx9tqHtf/vWCBBk/l7JN5i7cAAB6U+rjy/VaXiN25rZBSDOoNworYA2ZUGcLuLo/JdNa1cuQnPjQWd1xxIv7zrImYf9OZttdI2AnChVIPLoYZOw1iVdeZsx2FJ6wKWMSngmIh1pBXuYKcWfpWh7AThEXgK+vM/z3gsAu7sFBkUautjmlXW9Ld1Gqr9LM1Jx1R59oHVXSzlhUTUNiZd7pjMp1ztZuG1lXj6lnjccTABI4fbZ81TFkxBOFCoR60G2GOFuTcYsaimBzjEHafL3chNy4vYc/kONo63YTd6KNYEi/Irxff/inpjmK6vbxfXXU0sBUzoCr8BHn12ELQg2TrAEYevJfHnspkXScoeQXlvVCKHQAJO1GBlDpiD2PFBBkUFKl17VbEbn896vPtLuS+NdDDrshx/4jdr1yvgCHA4Kmc7ih58vJ+jDHtuXRlAwa4ROzGcfTbVdENG7FHmJ8Vk3O1Yrz+vn6/1koFCTtRcZR6ZmaY+0SQcwtRE8Ie9j5UyC8Sz4g9y62a8CpiAFDcLIOc23fwVE53lJp2S0KYzToLfgEuwl7tfm1uQqleh1tJATf8Bk+THhG75y8yitgJQk8paqnIPnaYo/nVBwHywtdhCbt9H7/+F3J9XqszZQNE7OKyAnnsfoOn0AuonJmTyXHtdepmrLp57ICxhqgO9e/klu7oBoO+nrogmc66FgHzithZLyk7CTtRccjf/adW7nSIiCykyXQWT7+zy3EMWVPCRMjBInajP5kcx5ItLdrytDrmv7Mbf1+x01WE3figLWktXK0jm8tZy/OpiMle+UlUPjNP4V3dEQCWbzuAA5pfCHI20OPLd+CJt3c62uitmOAR+2PL3sf9r251vIfZHMcL7+2xzWT1w8uKSWXcrRiviL2Xxk6pbC9RecjCcvOjK7GtpRM3zZ6Uf136vt3xzFo89MY2DK2txmlHDbW2FxrzBxF2uc1V85bg40q5X512vrOjFTf8+W0AwMThtaH69G//94qtpoqzP/p64UC+7k4uqMceIGIHgNufdM4hkOcfvLBWnz+uyw33KnX7jYuOwQ/mr7Wef/Pxd1zbXv+HZa6v6aiKRTBiYDUYGA50duPa0xut1M3OVMb115s6u3TSEXXYaKbAksdOEC54VQAE7FHnztYkADgmBcltSu2xq194Xd1xFZFBAwDbXXLVVcQvEy9RB0RdE/1kGjEOoK4O5EXQXHAdM8Y3eL6ui9i9skyuP2sijh89sOD+eBGNMLwxdzZen3s+1v/gEtx+6bHWa2ItVvmmLdIjBym22IKvn4NbLpgMoPeyYihiJyoOVVzVn7dBxEluE8bTDhKtqj/Rg3jshfxE5zyYUGRz7nXR25Vc+yBZMUF9avm4Ar+FJnSziv2yiHqyIoSbrdLalUY6m7MNsIrP5aABzvEOUWiOUcROEHpUsVC/LEG+6HKbMLoQZNarmhIZxGOXB+qC9idwtchczrVwmrBogtazYYzZBji9JvEAzl8vVTHvpeF0wu6X918ODpoRu26AtUEzkC3eht66EhJ2ouJQB+9U3zKI4NmEPVQee/h0QHUX3REK8V6DjvlmOXeN2K3JQyEqUMq/SBIhy9D6rSCktWL8IvZQPSgNrZ0iYndejy5iFx848tgJwgU1aHZaMf7HsFsxIc4dxGNXOuiVtSPwi3x1BI/YuWvE3p5UrZjgWT8AUB1yceaCrJg+GLGLipm6vjXUVDm2WRE75bEThB5HxB4JH7Hb25TYY1f6p4pVqeZXhRL2dFabDy5slTBZMbKw90bE7mfF9HTZZS90Vow6eApIHnuP98iABk+JikMVV/XLwgMkbdhkXXry5ze341OnjLee/+S59fZz+6jy3Qs24N6FG23b1BQ+WZDveHYtUukcLp822vWY5961CE0tnfjGRcfYtnd2Z3H5va959gcwbjTJdA4DqmPocFkQ+rVNLZi3eHMAj90+iSjhMXlIh5+w/++/1jm2uVkxXrNtewvd7FRRZlimxvxlUxPy/SqU8r8zBBESVVzVwdNAHrsk/vLhbn9ytU3Y7120KVTffvHiRsc21d+Wu/ebl4286I9MHeV6TFGq9y7lJrNu9yErP1rmtIlD8caWFut5zozYh9RWeU7Q+dEz6/AfM8a6vg4YE5TkiD3uMYlHh24FoTlnT0TTvg48/54+tz3CgIevm4VMlmNgTRwftCWxs7UTH506BkDPZsWo3PepGdiw5xB+bv6d5eXz7rlyGrozOW3my/VnTUQ6y/HZ047slX6SsBMVh3Pw1P56kO+5LY+9h4ffgqyzGbCESSA+MXOsTdjTOY5UJudZTEvAOcegmrjn7Ne0tIhGWM9YV4/8tkum4I3NLe7CHmE4a9LwcCfqIS47aRQGb44DLxrP49KH77jRAzF5RL12v0Q8ilsunNwbXQRAHjtRgahWjHdWjF60bYOnpV0b24FXxC4opLCZ2w1JFc+kab/UBih/y+E9WKl67GHRHZv51D73z4rRvw9it1LXQJd/dUQlj70vjfGSsBMVh1/EHsiKsT3u2Yjdy2PXbQs6GOjWTBX2jm4j82VAtX/EnuPcMyWPwfgFUChuA6FeBbcKzWMXAuyXiROWmPT+ylZUb00+CkLgK2aMPcAY28sYWyNtG8IYW8AY22j+P7hnukkQeRyDpwVMUCq0pEAhqJkepYrY3aiK2d8PUa6g1qX8rZwtk+PeES5jrKiSAm7Rt1fBLb90R7e/X08Ju7wgtdy33spRD0KYK34QwMXKtrkAXuScT4LhOs0tUb8IwhXVCSh+glIpeuVOII+9gE647eEWsbuVvx1cm8+7TqaznkLKOS/KinGLvouzYvSI3XQDtsUgH0/+peHXz94k8BVzzhcDUJcJvxzAQ+bjhwB8rET9Ig5zOlIZfNCW1L6mCuO2/R02+0IOfreZGSVNLR2ux9BZMZxzbGl2ZpwAhvi9vnkfurqzyOY4mvZ1aNsJVI9dF5wXsriG2z6ysMejDFuajf65lb8dIgl7074OeCW6cNhvVGHtBzf99lrkuVArRvTNLWIvVIflvsZsVkxhx+sJir2VjeCc7wYA8/8j3BoyxuYwxpYxxpY1NzcXeVqiv/Mfv3odp97xovY11bZ44u2duP/VrdZzWfBEOuCPnlmHN6VMEb+Iff7q3Tj/py9rz//g60341G/fxO9e2YJ7F27CuT95CZtdbgKAc4KSzkMvxIpxi5zdRLJak18NAIMH5IV94952T1uEc/t5G4cOcG2ryzN3i2q9I3bXl8w+eQ+eugl7oYOq8Zgs7JIV04dGT3tt8JRzPo9zPpNzPnP48L6RukT0XdZ9cMj1NZ2VsWJ7fqEJN1djixRZ+1V33LzXPQo/YK4EdKAzjbeajB+xOw90ubZXrRhd94LOIpUFMEg98P++eIp2u4yaBqnTp6ljB+GcycOR48Z6pqdNHIqnbzwTP/nEVKz+7oex+rsfduzToKmZ4jp46iXsIQTzspNG4YrpRn672MvtunU3sCBRt7vHHribPU6xwr6HMTYKAMz/9xbfJYLwRmtBSF8qN5GUv3d+1R0H1rinBgprJZnJ+9GiomNNgNopuv4Hta2zOY6xg2sAeETs0uDpyEEJ67GbQKpiq2sXj0YwvL7aitir4xGcMGYQaqtjqE/EUa9ZTFtXM8WtD8Vkxcjv5tiGGtQpvxTcZrvqzhnEJ5czYWK2dMe+o+zFCvs/AFxrPr4WwFNFHo8gbOjESzfQKH+lwtaK0TXXCZVALFqRTGetSFMU/goyZVx3X8q6jBHoEFaLW1102YqRo3G3qFjd6iZQEQYrYvfyxAW6milhsmJEd8MMStYnYlb/hccec+mr7hqC2Cl2Kyb/uCKFnTH2CIA3ABzDGNvBGLsOwJ0ALmSMbQRwofmcIEqGrtqfLlCVB/HchFH+3vmV7a3zyPkWwp7K5KyILcwKRLrzhSlkZf1KcInYZeshIdU/dxM49cy6qJrDXO/UzIoJkmmiE/YwWTFim6/YShdQVx1z2Cnqsa0bhu6cQSJ2KUqP9dEJSoFLCnDOr3Z5aXaJ+kIQDpLpLOqU/GudlWG3WUJG7KH7ZAhqKp21ytYKvzvIQhy6FmEGT0Xk7TZRSI5EEwEidrVD7pZNfvA07jeiCX1dcrfddMczImAeMmKP5yN2c5t63Yl4FJ3dWVs5AKt/QSL2iMvgaSVG7ARRDnRrdWqtGJvHrj8Wk+Q/Z4vYnW29dFbkhSfT+aXRhMceZCEOfa2Y4MIuhNstYpfFKYgVo6Z76lpxzsEYQ44ba54GsWJ0Kwm5CadXxO47QUl6XJ+IWe3FZ0LdP2HejKPam4nnqYw2UqP+MEGJ6Ge8unEf/r5iZ6+ci3OOexduxDYln/yJt3fgtU37XPcTVsyOA52454UN4Jz7RuyudojNiuFY+X4r/vBGk6P9m1taPC0VsU6o4bHb/W51WTwd+9pTuOPZtfjHql3WtjDriIqf/0EmCsmDuVE3K0Y5tVtueoQZ71t3lrvaOjK6cQo3W0V3oxCRepg0wvpEXLrJmx67sr94rqtMGXZRD7nfrA+paR/qCtHbfOb+N/G1v6zslXM1H0rhJ89vwDUPLLVt//pjq/Dp373paC++fCJi/+LDy3HPCxuxZV+HS8QuR+MBsmIAfOy+1/Dtp951CNuV85Z4R+ymsKcy+YhdiGwmZ1RH9MqO+X9PrsFvXt6Cmx5ZYW1T68l4kT+ns5PnTznCNuGoWvLYp49rwKwJQzC8vhpXzBiDy6eNxu8/d7Lj+t20ze6xB/Oir5gxBvdcOc3aFmUMt2qqHOo8eyHo/otZGxcwdnANpo9vwBfOmIAPHTkYV55slCAeUluNsyYNw7C6anx0ar7uvS4r5sxJwzGrcQjmXjLF8ZoOufZ6X4rYqWwv0SsIoexyWehBJR6NIJPLWn622I9zvW3hlspoa+Mi/rpl47x8erGcXDKdtX7OJ9M55HIcnANfOGMC6hMxfO/p97T76xa7EPVkJgyrxVZlJquRjZJ/Ln4lqO/DoJrrcN9aAAAgAElEQVQ4HvjcybZtcgRaWx3DY188zXHuR9/arpxPP3gaYcb/mWwwKyYaYbj7k9OsGyFgiPWNsyfhpws22M/pNXgacOz04etOQSIeRSIexd++fDoefM2YtBaPMjx83SlW+1k/fME8vvMa6qqj+L8vOd8jNwZJKZ0VWVKAIIpBfOaDGg5itmDKjNjFfiLlznmC/MNA6Y6Si9HW6aw97uV5W1ZMJj8Al0xnkTYPGot6l6HVIYRdF7nKUbc4PuD02HU3I9mGcPXYHVaMvo+MMeRyZrpjgMJa4gYhvxdBxC8v6MGsGNF/tZU4jtvHQXfYsFG3nPnTh3SdhJ3oHcRnPmhWn4gIhccuxJox5jvQ6D54KrfJNxIzSYMcA8hH3Ml0frWcVDpr9SsWYaFT38R1xmPOHdUp8X5ZMba2LrMkZdSj6Dx2zg3Ry3FjJm2QiD2iCLSxzXc3K0MmFtCKEajN3MYKvNIhw96QZWHvS1YMCTvRO1if+WDKLmYLCo9d6HCEueSx2zJewiUwHtBE7F7HEAKeSmetdqlMzvK8oxEWujiWKDugi9hVERUDl0HK58ZcZknKBPbYmdxP/+vT5YsHET/xHnjlm8u41dMPKtLFZLbIN92+lMdOwk70CmGr0oqoTfjf+VXemdZysE8+0p9M3prj3Br4atVE7EHSD5OSmCcdEXu4b7mwnHSRsJrjLZ4HSa2Uxdy9uJf9OF4eu7CMAnnswkqRDhdEbKti9nTEoFkxTDFj3PayrBuzQbyIiF2GInbisENEtmGtGDF4Kvbj4AFKCnj3QbQR+cytmoj9oMean4LuTM7Khkmmc1aqYywaCR29WZGwxrtWI20x6Kf2UXfZso3hF7GLSNlNnyK2YwX32OVfL0FsFfFrIKhQ+n2m/FbIcstLD8thWd2ROPz4+K9ex8lmBkLOEuZg5IXdbsXkXLJi/rp8BxrnzjfauCi7vWY7t6bby/nkAp09IzPQLDT1+PIdAICudBbn/+RlAAVG7B6DpyeNbbA9FxHmEwHmIMhi4z5ByUDcVNTIVyALdCArRnM+L/GbPKLO6Ke4wYj++Si3WEA6URVOzsR1ymI+pqEm1DH6KiTsRI+xbNsBNB9KAciLbdCaKMI1yCr75Tj39dDdInY579sYDHS2OWnsIAD5G4oQG5XPnzHB9vxQMm1ly8SikdDRmyXsSsR+4/lH4+5PTsUDn5uJEQOrzeO7HNvnrXUdPDXfT3Fu2foR7wc4R1ZKJQo0eKo5nRqxP3PTWdbjR+echr/MOdX6xSBuJH4fmZ9fNQ1/uv4UHFGfsG13z+6xPxc3vGnjGvDZU4/0PpnJ87ecjRdvPSdQ23JAwk70CoUuP5ezBF0ch/v63243D3k/zrnWo77w2BEA8hOGLjlhlPZYjcMG2KK7NskWKSQrRnjXqsVx5tHDUB2L4vwpIywxDWKD6NDNtASkiD0qhD3f7gLz/eAADiXz+ehB89j9th03eqD1eEhtFU6ZONTxe8HPSqlPxHHG0cNcX/f77IlfVxceNyLwDXnyiHocNVx/0+8LkLATBS3LFha/NT3VnGzxM1lsFl/ubM6eg67D7UyykOe4vnKkWGVI1Fx3sy8aaqpssw5bJWGPFmDFdGdyiDDn+ez2R8SzT364iZblsYuIXS5LK0XvB5P5awySx67LDAoinMzy5u39C4ubpeTMAuo73nipIGEnAmVXFIs1eOryetIldU8ddM1x/eCpbh8V2UrIca4tMCYmAwnR1xWKAozKhfLEIXmSk5Hu6NlFBylz0Q63ErNAPkr2WrquEMS7JURc9vnlKF+O2IN47LqB0jCzM0XLYj+efrv3Q10nYScKW28zLFwRaBVVZMWXzfLYze25AFaM28v2iJ3rI3Yx49W0YtyEaFBN3Baxy1ZMZ3e2oIidMeY4n/xURNQBq+8GRvxthIi7Lf0mZ+EU6rEHuSc5rJjCQ3bvl5m9WX8SeBJ2IlBFwmLgnNs8ch2qsItm6uAp5/4TkNxez0iDp8JqURFWjEizdPOzG2riVrokYL9pdKQy4UsKZHOIMub4hSDbGUJwuzS/NEqB6LMs2uKcnAMHpYg9ULpjgQtZqIJcdMTuUzLBsn787gQVBAl7BfDCe3twKOmdfrdp7yGs2dlW0PHfatqPhev2WFHnG5tbsKW5Hc+9+4Fnn7Y0t+O1TfvQ1pXGwnV7PPrWjqVbjUWfDyYz2NLcjkXr9tq8fTV6zg+a2iP99lQG7+xwv85/rtqFVzboywDL4vvQG03aNgnLijHE083PHqgIu0x7KlPQ4Gk04ozYIzZhN76unQELqQVFvLdCxO3Cbjzm4ErEXqAVE8RjF/9b+/fsL8p8Fk6PnqZXoeqOfZz393fi+j8sw4XHjcBvr5np2u6CuxcDAJruvCz0Ob7w4DIAwLnHDMeDn5+Fq3+7xHpt/k1n4vjRg7R9Epw1aRhe2bgPS78125FyBgAX/myx7fn5PzXyvd+47XxrW2fKLlY5NWI3ty9ab6yXnohHrKha5kapFK6K7LG/tL5Z22aEufizuNGcdtRQbbt4NOK6SPI5k4djZ2uXaz90pMzBUzXdUdYa8dpJYwfhSZ8c9uH11Zh55OBA5xYD00J0bYXDJJH/8rlH4Qfz1xp90UTsHz5uBFa832qluOpslyClFv7zrImY+8Rq3Dx7Em58ZAVOVPL4S4XoCQ2eEr2OWK1HXaCiJ9jc3O48f8oZHapWwMY9xn6ZEItFAMCBjnwE2KbMohTRfJbbrRhRvvfG8yeFOpeufz+/aprt+fO3nI2B5uIQwqqZMKzW9Wapi9ivmD4GJ4wZ5BCLqeMabDeyez81HVfPGmc9T6aNwVN1ndCIJitm7OABtj49feOZAOw211vfugC/+syHtP1WEbsJEZftIDkyv/6sidaKTDqPfd41M/HWty7Q9l0QJGK/atZ4NN15GT4ydTSa7rys4ElDYeW6P8k7CXsfRwSZvRVVqB54kNMKjz6sryyLuUPYhRVjLRJtbBdT+AtJ0VSzf+oT9h+sDHnxFFP8vd53MXgqL6qRr2hob8s5tx3LsHzyz1OmFdOgrBMqn16IqbpyUpCBTC/En1xYJ7YyBGbYrbYJNnhaXFZMqXDLgxdb+6MVQ8Lex5HL1fY0DM6SuEHOKmZ0hu2hXC63tcteiEtE6lYeu1RFEShsQE29toRS55wxZqX8icFcr3uVSHeslRbbdlvOLce5TTgS8ajteTJtZNKoEbtN2GNuwm4OcLp31RNV+GRBdqRfms+rNOWFVXTlcEucqemJ33dGfLfIiiHKRqk/em4Rb5i1NwXdBYptS4ck7EptFjlvXX4u+he2NK+xr10Qq+OqsDvrwHuJg8igqauWI3bzf2U/Uc9c3VeQTOd8rRgh4N2ZnonYLaHT1JeRyyYb24NMUMo/rjNvfsUU2SoYl4+K+DVsDdb2IzOGhL2PY32hSvyXSmtSHDm4ZUEIggQzQjD9Jg6pHJCEXa1UKKJrdfC028xWKUTYHRF73DlQKayYlOl5eyEi/gFV+YjdWvVHk7Inb0rEog4Z8YvYq62IXYmwixRLrvzvVWbXSokMMPNUtl2E7dWbVozfmfK/hnu+L70NCXsfx/rwlTiacJvko0a1OlRNFd51WN97v0fELq5bHTztVlZUCoPqsauDnxHGrBWMRJaKjlpzAFHsr5vQ44zYFY897vzqGR57lW2bLt2xW1mjtWixVOYYyMdTbxpB1yFV9xURu25SWE/j9knJ12U389j7kcBTumMfZF97ClfNW4L7r51ZVFTx/Lsf4P5Xt+LROac6LIWfv7DR0Z6BaYQ9v9+hZBqf+u2brml+tz+5Gm1daRw3aiBGDnKmParIwt7WlcY9L2xAV3cWt116bH7QNMexeEOztRydiFarovocci9EiV1BdSxiWyhatmIyOa6tjQ7AujYhzrKlE3ERiZwi7NWxqGaiTF4ArW3SYxH1qmIrnhY6QXOAaSWJz4jWijGfN9RUYc/BVKDjytc7clAC6z44ZM0P6A3E30+9gddVx7AHKdSa1y3eV7e/dyVCwt4HeXrVLmza2477X92Ky6eNBlDY4Omch5cDMKJztdTrbxZv0e6Tzrirw7aWTqz2mAT1ykZjYpCYQFRfHcMhaYV6FXmiTWtXN+4xbza3XXqsJbY5znHLX1Za7UTE/vkzG5HO5nDvok2ux/cjEY/i2ZvPxkX3GHn2DMyWw62LSudeMgX/dpJR8fGCY0dg7e6DuPTEUdYELPeIHWCSbqgeOwCcMGYQhtVV4bZLpuCOZ9cZfZKO89XzJoGB4cqTx9n287Ni/vCFWZ6R8l0fn4pHl25HeyqDpVv322446nXc/7mZmP/Obowc6H/jlrv1009MxcNLtmH6OCO3/uHrZmnnIZSSS04YiZtmT8L1Z02wbX/w87Pw7Jrd+PiHxuHhN7bh+rMm4Ncvb8ZVJ48v6DyPf+m00PMWepr+c4vqR0SkAatMgRknMmG8b6f37j471A+/e5H866Cty34DkLNiZOESYwB1VTH810XHhOqPSiIexTEj6239ZYxZ1oouW+JL5xyFsYMHAADGDRmAH398qq1krCrs4nmOc9vfMB6NOOy1K2eOA2MMXzznKG1dmJqqKP7romNsxcfkc7ml9Z09eTguPG6E/k2AMZnpxtmTJI/daS0Jm2bs4AH44jlHBQo0ZH9+aF01vnbBZOtvedYk7z6Vglg0gq9fONmamyAYN2QA5px9FIbUVuHmCyahtjqGWz98TMER+8zGIbh82phSdLlkkLD3QcTXISfVDC9mfCxMKRjVipGfpkLWKPGz3LutzBOgTVl3VF5YQ/Z8jWJZpVmGLKHO8jQPKeyYoN51PBpxjdSj0k1azXBRD6+b8BRIQEtkDgv7K6oR9kLoj2mElQIJex9EfJlzPD/gV0wee6iIXbFi5EHWZEh/1K+4mLiJDK2tstUzl8+bzXHbTa07myu4HrmKWshKvMdC2MO85eImoWYeRq2/JXcMhKqHlwdUxWtBLpWV6Fuc1YznFPNek7CXj5J47IyxJgCHAGQBZDjn7kVNCF+sn9acWwtQFGXFBMxW0aU7ytknYT1Rv18K4lyDB1RhX7u9nIFVK4ZzW3SezuZKXo9cEFEi9jC/ChLxKDq6s47oWR58VGeRqjdrbcQe4C9fqojdml1assWdi+0RUSilHDw9j3OuL6tHhEJeOSZvxRT+BQtazzqT5RorRhb20kbswooZUlvleE3OipHFpTtTuohdRYholYfH7oblibvM1FRnnuoEU5dtFKQL+UAgaG/1ZDWftWLuGeUoH0AYUFZMH4Jzjm0tnfn0NeQXlXD7juw9lMTu1qT1fHtLJxJVEdsklu5MDm817cexowaipT2FI4fWao+VyuQcwr6saT/GDxmACGOB09wEvh57Vi/s21o6LFugK53FXum8u9uSjnoqpUK8x8KiCXP/cPPlrcHTnP+NQo7YrYHMQEvJBe+nF7qZp8UEFL1RBoPQUyph5wCeZ4xxAL/hnM9TGzDG5gCYAwDjxxeWVtTfeWzZ+/jvv63GJ2eOBWAIY9qnGNVVv1mCLfvylR/PvmuRo83fV+7Ej55Zl39+wxnaYyXTWUcFxF8s3IRfLCw8pdALcW1jB9ur951z10vWY115XXkyk19KZRjEWyxyrfe15wd0G4cOQFNLp+u+Ip3UbUKPOkFJPp9ArV0DeFtwE4bVYuu+DuscF58w0qO1PzPGD8af3tyOY0bkM4WG1hk33XMmDw99vLKUDyAAlE7Yz+Cc72KMHQFgAWNsHefcVoTbFPt5ADBz5syerZxfoby+uQUArJxYzuEbscui7sYBZVbntpYOTBlZjwhjuOykUbjrufUADKFVPfZiiUachcW+85Hj8Mcl29BuCvKsCUPx0alj8L2n38VbTQdCHf+N22fjhO88p33tha+fbU33P/3Ohdb2H/77Cbj0hFGO9sKKueWCyZj7xGrba8/cfJbnGEPMJSsmZlkxzl8Aqn9erR08dRfH+TediWQ6h3g0gqW3z3bMWg3LFTPG4JSJQ6x0TgA4oj6BJbfNxvD66tDHI10vHyUZ3uCc7zL/3wvgSQCzSnHcww1R+7zWFCMOns9jL+JLktbkn+c4x/ghAzB+SP5LnM5yR4GpYtH5xic3DsGAqphlF0UjwIljB2FqAQsq1FXHXGfCDq9LYHRDDUYr9bzrqmMYrPH1xXustgeMejC6sQBBPm/dvt2K2MF9rYmwHrvcpyMGJoqeOckYs4m6YOSgREHRN2XFlI+ihZ0xVssYqxePAXwYwJpij3s40mkuqlFj1iLxGzwNWptFjcIjzIiioxHmGIgs9XqaOrGKRhiiEWbdcMS1BVlHU4c8LmC7HBddcRNY0Y9CqiW65bGL50H+VLp+VbI2lmKuAVEYpbBiRgB40vxQxgD8mXP+rxIc97BDTLGXF37OemSWHEoG85bVAdEIY4Y1EGEOMe0skV8tMGZJ2q2geNQQdnHD0S3JFgZZNOPRiDVD1u1wbmcR24PUGlcR1+CoX25lODmVPYhoV3IpWcqKKR9FCzvnfAuAqSXoy2GPiNjTUn1zYVfoInZ1cQo31FIAjBnefZQ5xbSjxAsl62qiRCMRu7AzvSgWQlVMFnb98dz0Rp15Gga1hIC6XRexB7naSg56K7nvlQ5NIehDiIhdzPDk8B48VZeTc8NRv9sUdiNiV6yYUgu7xoqJRRiijEm15oUFUgJhl0TZTdjdBb/0VgwrMmKvZJ+arJjyQcJeYv705jasNqsbtqcyuPPZdbZSpZxz3LtwI97f34mt+zrwq5c2W69Zwm763P9ctQv72s0V3xnDluZ2zFu8GY8s3Y6V77c6api7odbvZoxZNVjUCNOvWmJYu0QtWAUYqYExTQ3zaAmmKsoDiGE1sZiI3e1Xh2fEHqCDFazrFX1TqnRoglKJ+daTxrhx052X4d6Fm/DrlzdjdEMC15zWCADYvr8TP3l+A+av/gDNh1LY157CZ087EnXVMavIlpxWJ8rrMgBXzluC5kP5yTq//PSMQH1SI3aRRhmNsNAiNqgmblvSzg99xB6xfenzg5bFC0GEMVx0/Aiks9x27hvOOwr3LTJuom6LiYizF9IPcU8SAn/S2EGYPr4B3/3I8fjJ8+vxxbOPct13WF01rpihrw5YyZN8yGMvHyTsPYhIHZRTCEXk1tWdsaJxMeNPZMDoyuMy5hzYDDrFX01hzOa4UZQqko/Ya6uigfz1+kQslLDrUvBiSjaO28Bjofzms85SRd+4aAr2HEzh8eU7kHLJRy8mK8ZaEs+8hkQ8iie/YkwEe/i6U7T7iKu97swJ+PK5euGvZG0sVXEyIjz01vcgcs0XgfBa5Rg6m7Uv2KxbZSbHndFb0ProarpjJpczB0/zAlufCDZNvy4RLhbQpjtGmc1/FZFdKWrAeAmhuMmk3CL2YqwY6+YUYifx+XBdvK2y7QyK2MsHCXsJUWdYio+1/MXVfYXF4haiPoouoszmuEO0Co3YM1ku5bEbH4H6gIKtLt3mh85jj0ci9pWKzE9hoXnswfuSX6hahxDRqmIi9hBiJlIZvYp3VfL4I5UUKB8k7CXEkS8ecX5xdSsiybXHAb1gqyvwGO2CRey6io05c+EHMYgZVNh1pWW90Kc7KhF7Ca2YIBG7X9mEeAF57LECrqG/57FTwF4+SNhLiCqg4oMtB/K6gbtMlttmkeqE3YjY7d+UQiP2tLBiIghtxYS1KXTtHR57Ka0YDyGsNvuivh/ibS0qj90l3bFYKlkcyYopHyTsJUTNPhEiIy9WoYsWMzlu88uTGu9cXUkIMDz2ILaBPmI3qg0KKyaodx5WfHUZJhEzj10gblilsGICeezK+yvEWPy9CrnBFCJi/V32Knl8oNI5rIV9xfYDaJw738o7D0tndwazfvgCXtlolJZ1Tt23t7/xkRW44pevO45z3k9ewo2PvG091614ZCzU4IzYdVaHinrD+fZT7yKVySESYUhUGfsPCVgZMOx3VeexA/pVeoSgDqpx/npQS/sWwhjzGMPq7JUKjx1llKnNR+4FCLvZ9yCrVYnrHDUoAQAYXuesnDhlpL1PlQhNUCofh3W644L39gAAXt6wFyeOHRR6/wOdaew9lMJ7uw7irEnDXX/iC5vln6t2Wa9lFAF4Ye1ez3Nlc06PPZXJIhGP+taMcfOUo4zhiPoEHvjcTAyqqcLDS7Z5HgcI7/meMmEIHny9yXr++8+dbJxb+tILf18I3gljBuK6Myfg8eU78MzqDzCmoQaPf+l0nHrHiwCAxd84z3aOV755Ht5q2o+vP7bKs3cfmzYGNfEoLjzOXrf84S+cgrW7DxZkwQjklZK8eOIrp2PkQEPQP33KkRheX42LjnfWUX/w87Pw3u6DrjfGnmbhreeUvCAc0Xsc1hG7+AoWOglErEcqpvY7PHaR9aDZN2iqoiCrSXdMpnO2BZABQ0hV3ErxCjE6f8oIDKoJeI8P8VbNGN/gyGM/deJQAHZhFxF6PnKP4PwpI6wMnDOPHmarBz5+qL207LghAzBtnH/JX8YYLj5hlGOAc3BtFU4/eljQy9IiDukn7DPGD7bKAkciRn90n7/BtVU4o8g+FcPE4XU4fnT4YIfoGxzewl7kch8i6m61hN1Zk8XtPGHXD81p0h1Tmaxj1R1d1orbTEvZFw4arYbxTWurY44+i+fyxChxbvEjRvjyok08xgKn/ZVrpqZ4L0u8TglBFMRhLeyCQrVA+KluEbs4sC6KCyvsusHTZDrn8NijEeaIkt0idlnLgwp7mLeqrjrmsG7UKojyqj8ZpYyvPBPUT7DLvSSXZcUErJFPED3JYS3sYuJQobnCQsjbzGJcqpdtTVDi3FHdT43u/chp1szsSGUcEXuEAQlF2FU/32obKSRiD9QMgH4yk9hf+OkDpYFS0U+RqSMEPsyEoXIN11kRe7E/AwmiBBzWwi7CvFJF7OpC0OL1TI4HnkzkdS61m21daYf1whhDdcBJRLIVE1Q8w1gdtdUxh9Ja0biI2CVhF++XLmL3o9x6GgmRFUMQPU2/FfbtLZ1Ysd1YGHnxhma0dnbjzS0t2HMwabURX8G/vPU+/vBGk1WL/IO2JH7/2lb8c9Uu7JXaZ3Mcz6zeDc45/rXmA0usxYIXqhUjItC1uw9iydaWoq5n49527GpL2rbtbks6Bk8Z4NjmhjyIqNZldyO8FaPsr0xGahiQF3bx/om+FDRhqEwhu+ii3+ApQfQG/Tbd8e4F67FqRxvm33QmrnlgKaaPb8CK7a0YNSiBN26bDSBfkGvrvg58+6l3MbS2GpedNAoPvt6EX79slHgdP2QAFn/TSK976PUmfO/p9/CRqaPxz1W7cOpEIwPFzYoRy9otWt+MReubA/U7HmWBbZq2rjQmDq/DtPZurHy/FYAhhqo944Zs7cjFur563tG476VN2iiYMYYrZozBE2/v9D3+xSeMdF3dXvxakHPWZzYa7+cV08cCABqH1gIAjhzqXGBZZcRA4zzXnTnBt20Qxg2pwehBwXPnP3LSaPxxyXYr6+dw5upZ4/HI0u3l7sZhTb8V9paObhxKZqyoesV2Q/h2S1GvKlz7O1K2/wGjfrpgd1sXACMCB4BtLcZrB5MZZHPcWtJORMJhffTPnDoe/33xFJz43ecBAD+/ahpufnSltu0dV5yIj0wdjdqqKBhjeGb1bnzlT28jwpg2M2bckBosuOUcnHHnQqvsrj1ij6Dpzsus5/910TFonDsfAHDT7En4xYsbARg3jp9+Yhru/uQ0HOjoxvTvL3CcizFgy48utaLzpjsvs44liGg89gnDam19uPb0Rlx58rhA9WnqE3HbvsXyyjfPD9X+lIlDS3r+SuaOK07EHVecWO5uHNb0W2Fv60ojlclqS+AKVNkVXrnfknNCDuVByYNdaWl9UmNbWL91eF3CZjt4CVptdcw2OCksAMb0pXJzOeN4suVSyMxAeRe3/YNksYjxCL9qkWGLjhEE0Y899tbONFLpnOegpRqxi6Xm5CXn7Eut2b1fWbjbutKWRyzaZXLhBkwbBsRtwq4TaIFam0RcC4M+Yhd9kTOACqlvIts3bpUMgwzEtpuLhoQtA0wQhD/9VtjbutLozubQ2W2fbu8llrqIXU0dBPIimpE89VbzfEA+qlWzZPwYWBOziaWXQKqimp9Fqx881dlChWQDyfu43RiCLC0nyiCEXbiDIAh/+qWw53IcB5N6W0WOZtWVa1o1wi6nDqq56BmXiD0/CzGcsDsGPT300SHslhWjT3cUfZOvubDUPNnK0bcIksXSnjLe43qK2Ami5PTLb9WhZMaKqg86hD0vOrJOD6qJ6yN2j9RBWdhbO7utCF3YFemwwq4KssfuamlZcS0RBlRFNVaM2Te5S4Wk5gWL2P2FnSJ2gug5KipiT2dz+PTvluCtpv14c0sLPvyzl/HM6t2OdiKvHHBG7HsOpnDVvDewYc8hW9XBoXVV2NeewtXzlqBTWtRZVNf7wdPv4bevbAWQF3Q54r350ZX4zj/eBSBPVgnnsQ+osguyV31ydeBSRPA18aj2ZiQ8dvlXR1irSBxfPafa7yBWTEST7kgQRGmoqHDp/f2deG1TC3YeWIUrTx6PDXvasXDdXlx64ihbO1nM5YFQwZIt+3HDn962bRtWW42lTfuxpbkDsxqH4PqzJmDOw8utyPh3r2612oqJTG5WhhA/nXB+7/LjsWlvOwCjTopIIzz9qKE4udFemXHmkYNx0+xJmD6+Afcs2IBVUt14NWK/5ISRuOG8ozDn7KOwraUDsQjDEQMTuOu59QDyHrscTftF7PdcOQ2jG2rw6qZ91rZbLpxsPWaM4dv/dhzOnjwMF9y92Dw+00bs//zqmVizK9//uz5xEv66bAdOHBO8guCPP34SJh1RF7g9QRyuVJSwiwyXRDxqDVzqxFUW81aX1EV1r6F1+WJU/1FW/CQAAA3XSURBVHn2RFx43Ah8dOpovLOj1bGvyOhwQ9QL0dVo+dCRg3HNaY3WcYSwf+OiYxxReCTC8HVTSPe3d+PWv67Kv6bYILFoBN+4aAoA4KSxDThpbAM6uzOWsAvk6NpP2D82fQwAWML+tQsmObJYvqBMCBpUE9cK+4ljB9lq3o8aVIObZk/yPL/KJ2eOC9WeIA5XKsqKOWQOiFbHo5Zo6krS+kXs8rEEsrCLae6JeATJdM5xDi9hH5iI2WrEqMiiJ0fdfr60OuU/SAkAXYndWkmYe6LEbCwSQdwj84ggiJ6nor6BIvqujkUsz1iXpy5H6ergqdVGEfyhtfmp78L3TcSjSGayvhOWbMepq84Lu0Y5bbM95dRGHzGMKSkoQeqi6/LMw0TshRCNMFQFrDtDEETPUBJhZ4xdzBhbzxjbxBibW4pj6hA1WRJSxK6bWSqLuZsoqysYDZMjdlPYq2MRJNPhhH1IbZV3xC4JdLSYiD3ArFFd1kptlRyxl17YI5GQRbsIgig5RX8DGWNRAPcBuATAcQCuZowdV+xxdQiBTcQiyJqDgSldxN6Zz4qRM2S8GCJF7AOliD2VybnaOfrj5IVdJ5xRSaDlafd+mSSqkLvN+pTRTfmvkSL2nhD2KNMPnhIE0XuUYvB0FoBNnPMtAMAYexTA5QDeK8GxbQiRjkWZFQ1vbenAki0tGDEwYZXk3dzcYe2z92DKeSANsscu8skT8Sg4B95q2h+4j0Nrq9CVzmLJlhbbDUbgFmn7iaEq5EGEXYccsfeEFROJsEDpjgRB9BylEPYxAN6Xnu8AcEoJjutAROypdM7y2JsPpXDVvCWOtuOG1GDHgS7sPRRM2MeYCwzXShHtkFpD7O98dp3nvmcePczKHBFLven6BLgLu5oLrqIKf1hhnz7eWOz51KOG4C/LjD/X8aMHhjqGH1NG1qOuOmYbryAIovcphbDrFMYRCjLG5gCYAwDjx48v6ETXnzkRf1yyHclMVpsjfs7k4fjiORMBAEcPr8OBzjRaOlJoPpSyyt8+/qXTcPOjK7Gztcu275iGGjx781m2MrIf/9BY3PbEagCGeP/f1dPx5T8tx5It9gj+d9fORPOhFKIRhkffeh9eqIOgr3zzPCTTWdQn8udd9e0PO8odFBqxv3n7bHRnchhWZ4jtv08fiw+NN/Llxweocx6Upd+ajbrqGJLpHEXsBFFmSiHsOwDICcZjAexSG3HO5wGYBwAzZ84syANoHFaLM44eimQ6px2YPGp4HU4/apj1/IiBCQD1eG/XQWvbzMYhGFJb5RD2SITh2FH2CDYejeD40QPx7q6DmDVhCAbXVmHc4AFYAruwJ+JRjBtiiKTfbNOoInpiP5lBA5yzMR0ee8AKXiMGJhzbSinogiPqjfNIa1MTBFEmSjHK9RaASYyxCYyxKgBXAfhHCY6rJRGLIpnOagf+GjSCCDjrvQRdOg7I10YRKZBu5xC0J70nLwXJZtHuV6QVUxSmF0+rvhFEZVB0xM45zzDGvgrgOQBRAA9wzt8tumcuVMcjSGWck4YA97ojarXDQhZvEILuV9vkkM+s1IKFXdkv6BqlBEEcfpSkpADn/BkAz5TiWH7oIvaBiRgOJjMYWKO/HLWmenXANUFlhPc+yMdr8IvYC420VSEvZJGMglEWGCEIom9TcQnH1fGoOc0/L+wDzBQ+t9mYaoReHcKKEQxMBIvYvcoNxCLMd8k4r31letWKIQiioqg4YU/EI9jXnrIWlAbsk250qKsmORa0CIAQ1gYfYe/wWPKtGDGOKtk0WTK8CYJwoeKE/ZQJQwHAymo5uXEw7vvUDEwd14BzjzlCu08sGsE5k4fjN5/9EIBwg6c/+NiJOLlxMI4ZWQ8AmDKqHseOGmjlu1+pVBz8zkePx4zxDThhjJFhc9lJozDF3LeYGZlyxD5rwhAM6cX0k6tnjcPkEXW48mSqrkgQlUBFle0FgItPGImGAXG0dqZxcuNg/PVLpwMAnrrhDM/9HvrCLOuxsGY+c+p4/HHJds/9po1rsM4BGGl9z958lmv7GeMH44mvnIHrH3oLgJEff9+nZuCUH73gubC2H8JjT8QjeOyLpxV8nEIYNagGz99yTq+ekyCIwqm4iB3I+9zqZJ+giEqKDTU9F/UK20VE2ol4tOCMGPl4PVDehSCIfkZFCrvwuQtN+RMZNbU9uJCyuOlYwh6LFpWiaFWFJGEnCMKHihT2gVbEXphQJtNGqd8wXntYRPKLGPRMxCMF/8IA8jNW1VIDBEEQKhUp7KLQlpopEpS8sIfPjgmLiNKrSxSxkxVDEIQflSnsRUfsYu3Unr980cfqeKTIdEfhsZOyEwThTUUKuxg8LVQoRw4yClaNqHcWyCo1osZLfSJWUP68dRzzWicfUV+SfhEE0X+puHRHIF+3RVgqYfn6hZMxfVwDTj96GJ772tk9EgWLGaZCkP/rw8d4zkr1IxJh+ON1p2DKKBJ2giC8qUhhF4OnhQplIh7FJSeOAgBr4lFPIX5VTBxeV/Sxzpw0zL8RQRCHPRVpxQiPvaO78Ai4t6BFJwiC6G0qUtiFx96RKsyK6Q24ae8UmrlDEARRKBWpOiLdsRjPurcoZrYpQRBEIVSosIuIve8KO6Ma5gRBlImKFHZhxXR2910rhiAIolxUpLCLGaNfPveoMveEIAii71GR6Y4A0HTnZeXugiecZogSBFEmKjJiJwiCINwhYe8hCl3blCAIolhI2AmCIPoZJOwEQRD9DBL2HoIGTwmCKBck7D2ESMkspgY7QRBEIVRsumNf5/9ddiyG1VXj4uNHlrsrBEEcZpCw9xANA6ow95Ip5e4GQRCHIWTFEARB9DNI2AmCIPoZRQk7Y+y7jLGdjLGV5r9LS9UxgiAIojBK4bH/jHP+kxIchyAIgigBZMUQBEH0M0oh7F9ljL3DGHuAMTa4BMcjCIIgisBX2BljLzDG1mj+XQ7gVwCOAjANwG4AP/U4zhzG2DLG2LLm5uaSXQBBEARhh5Vq6jtjrBHA05zzE/zazpw5ky9btqwk5yUIgjhcYIwt55zP9GtX1OApY2wU53y3+fTfAawJst/y5cv3Mca2FXjaYQD2FbhvX4Sup+/T366Jrqdv43U9RwY5QFERO2PsYRg2DAfQBOCLktD3CIyxZUHuWJUCXU/fp79dE11P36YU11NUxM45/2wx+xMEQRClh9IdCYIg+hmVKOzzyt2BEkPX0/fpb9dE19O3Kfp6SpYVQxAEQfQNKjFiJwiCIDyoKGFnjF3MGFvPGNvEGJtb7v4EwZyRu5cxtkbaNoQxtoAxttH8f7C5nTHGfmFe3zuMsRnl67kextg4xtgixthaxti7jLGbze0VeU2MsQRjbCljbJV5Pf9jbp/AGHvTvJ6/MMaqzO3V5vNN5uuN5ey/G4yxKGNsBWPsafN5xV4PY6yJMbbaLDS4zNxWkZ83AGCMNTDGHmeMrTO/R6eV+noqRtgZY1EA9wG4BMBxAK5mjB1X3l4F4kEAFyvb5gJ4kXM+CcCL5nPAuLZJ5r85MGb29jUyAG7lnB8L4FQAN5h/h0q9phSA8znnU2Gk7l7MGDsVwP/CKHA3CcABANeZ7a8DcIBzfjSAn5nt+iI3A1grPa/06zmPcz5NSgOs1M8bAPwcwL8451MATIXxdyrt9XDOK+IfgNMAPCc9vw3AbeXuV8C+NwJYIz1fD2CU+XgUgPXm498AuFrXrq/+A/AUgAv7wzUBGADgbQCnwJggEjO3W589AM8BOM18HDPbsXL3XbmOsaY4nA/gaQCswq+nCcAwZVtFft4ADASwVX2PS309FROxAxgD4H3p+Q5zWyUygpsTucz/jzC3V9Q1mj/bpwN4ExV8TaZtsRLAXgALAGwG0Mo5z5hN5D5b12O+3gZgaO/22Jd7AHwTQM58PhSVfT0cwPOMseWMsTnmtkr9vE0E0Azg96ZV9jvGWC1KfD2VJOxMs62/pfRUzDUyxuoA/A3A1zjnB72aarb1qWvinGc559NgRLqzAByra2b+36evhzH2bwD2cs6Xy5s1TSviekzO4JzPgGFL3MAYO9ujbV+/nhiAGQB+xTmfDqADedtFR0HXU0nCvgPAOOn5WAC7ytSXYtnDGBsFGPV2YESKQIVcI2MsDkPU/8Q5f8LcXNHXBACc81YAL8EYO2hgjImZ2XKfresxXx8EYH/v9tSTMwB8lDHWBOBRGHbMPajc6wHnfJf5/14AT8K4+Vbq520HgB2c8zfN54/DEPqSXk8lCftbACaZo/tVAK4C8I8y96lQ/gHgWvPxtTB8arH9GnMk/FQAbbyHa++EhTHGANwPYC3n/G7ppYq8JsbYcMZYg/m4BsAFMAazFgH4uNlMvR5xnR8HsJCb5mdfgHN+G+d8LOe8EcZ3ZCHn/NOo0OthjNUyxurFYwAfhlFssCI/b5zzDwC8zxg7xtw0G8B7KPX1lHswIeTAw6UANsDwQL9V7v4E7PMjMGrVp2Hcfa+D4WG+CGCj+f8Qsy2DkfmzGcBqADPL3X/N9ZwJ46fgOwBWmv8urdRrAnASgBXm9awB8G1z+0QASwFsAvBXANXm9oT5fJP5+sRyX4PHtZ0Lo5R2xV6P2e9V5r93xfe+Uj9vZh+nAVhmfub+DmBwqa+HZp4SBEH0MyrJiiEIgiACQMJOEATRzyBhJwiC6GeQsBMEQfQzSNgJgiD6GSTsBEEQ/QwSdoIgiH4GCTtBEEQ/4/8DgaCWS1k2s+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ddaa6f2b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********************************************************\n",
      "\n",
      "fc1_units:  48 , fc2_units:  32\n",
      "train_numb:  1 eps_start:  0.992\n",
      "Episode: 579, elapsed: 0:09:29.603803, Avg.Score: 13.00,  score 18.0, How many scores >= 13: 51, eps.: 0.10\n",
      " terminating at episode : 579 ave reward reached +13 over 100 episodes\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ddaa10f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********************************************************\n",
      "\n",
      "fc1_units:  80 , fc2_units:  88\n",
      "train_numb:  2 eps_start:  0.992\n",
      "Episode: 572, elapsed: 0:09:32.363203, Avg.Score: 13.00,  score 18.0, How many scores >= 13: 65, eps.: 0.10\n",
      " terminating at episode : 572 ave reward reached +13 over 100 episodes\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ddadae8d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********************************************************\n",
      "\n",
      "fc1_units:  64 , fc2_units:  56\n",
      "train_numb:  3 eps_start:  0.994\n",
      "Episode: 590, elapsed: 0:09:39.475603, Avg.Score: 13.02,  score 18.0, How many scores >= 13: 57, eps.: 0.09\n",
      " terminating at episode : 590 ave reward reached +13 over 100 episodes\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ddb067160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********************************************************\n",
      "\n",
      "fc1_units:  80 , fc2_units:  88\n",
      "train_numb:  4 eps_start:  0.989\n",
      "Episode: 633, elapsed: 0:10:47.455223, Avg.Score: 13.06,  score 20.0, How many scores >= 13: 58, eps.: 0.08\n",
      " terminating at episode : 633 ave reward reached +13 over 100 episodes\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ddac08710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********************************************************\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "list_fc1_units = []\n",
    "list_fc2_units = []\n",
    "list_eps_start = []\n",
    "list_episodes  = []\n",
    "\n",
    "numb_of_trains = 5 # 10 \n",
    "for i in range(0, numb_of_trains):\n",
    "    #generate random number of nodes\n",
    "    fc1_nodes = random.randrange(48, 128, 16) # possible numbers : 48, 64, 80, 96, 112, 128 ( > 37)\n",
    "    fc2_nodes = random.randrange(fc1_nodes - 16 , fc1_nodes + 16, 8)   # possible numbers  with step 8 \n",
    "\n",
    "    #randomly initialize epsilon\n",
    "    epsilon_start = random.randrange(988, 995, 1)/1000.\n",
    "    \n",
    "    print('fc1_units: ', fc1_nodes, ', fc2_units: ', fc2_nodes)\n",
    "    print('train_numb: ', i, 'eps_start: ',epsilon_start)\n",
    "    agent = Agent(state_size=37, action_size=4, seed=1, fc1_units=fc1_nodes, fc2_units=fc2_nodes)\n",
    "    scores, episodes = dqn(n_episodes = 2000, eps_start = epsilon_start, train_numb=i)  # train with current params\n",
    "    list_fc1_units.append(fc1_nodes)\n",
    "    list_fc2_units.append(fc2_nodes)\n",
    "    list_eps_start.append(epsilon_start)\n",
    "    list_episodes.append(episodes)\n",
    "    plt.plot(scores)\n",
    "    plt.show()\n",
    "    print(\"\\n********************************************************\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Testing session and the average score\n",
    "\n",
    "We load weights by the function _torch.save_ of the package **PyTorch** from one of the saved weights files:\n",
    "\n",
    "    file_weights = 'weights_'+str(train_n)+'.trn'\n",
    "    agent.qnetwork_local.load_state_dict(torch.load(file_weights))\n",
    "\n",
    "We run testing for each file _weights_\\__i.trn_, where i = 0,1,2,...\n",
    "The testing procedure is implemented by the function **checkWeights**.\n",
    "The function **checkWeights** constructs the **agent** with \n",
    "the parameters _fc1_\\__units_ and _fc2_\\__units_ corresponding to the given weights file.\n",
    "For each testing session, we run **checkWeights** several times (=6 in this version) \n",
    "to get the average score for the given set of parameters.\n",
    "\n",
    "For each session, the output will be looked as follows:\n",
    "\n",
    "Train: 0, Test: 0, Episode: 569, fc1_units: 48, fc2_units: 40, eps_start: 0.992, Score: 19.0  \n",
    "Train: 0, Test: 1, Episode: 569, fc1_units: 48, fc2_units: 40, eps_start: 0.992, Score: 17.0   \n",
    "Train: 0, Test: 2, Episode: 569, fc1_units: 48, fc2_units: 40, eps_start: 0.992, Score: 16.0   \n",
    "Train: 0, Test: 3, Episode: 569, fc1_units: 48, fc2_units: 40, eps_start: 0.992, Score: 15.0   \n",
    "Train: 0, Test: 4, Episode: 569, fc1_units: 48, fc2_units: 40, eps_start: 0.992, Score: 20.0   \n",
    "Train: 0, Test: 5, Episode: 569, fc1_units: 48, fc2_units: 40, eps_start: 0.992, Score: 18.0 \n",
    "\n",
    "       Average Score:  17.5   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 0, Test: 0, Episode: 585, fc1_units: 96, fc2_units: 88, eps_start: 0.99, Score: 13.0\n",
      "Train: 0, Test: 1, Episode: 585, fc1_units: 96, fc2_units: 88, eps_start: 0.99, Score: 16.0\n",
      "Train: 0, Test: 2, Episode: 585, fc1_units: 96, fc2_units: 88, eps_start: 0.99, Score: 18.0\n",
      "Train: 0, Test: 3, Episode: 585, fc1_units: 96, fc2_units: 88, eps_start: 0.99, Score: 14.0\n",
      "Train: 0, Test: 4, Episode: 585, fc1_units: 96, fc2_units: 88, eps_start: 0.99, Score: 13.0\n",
      "Train: 0, Test: 5, Episode: 585, fc1_units: 96, fc2_units: 88, eps_start: 0.99, Score: 17.0\n",
      "       Average Score:  15.1666666667\n",
      "=========================================================\n",
      "Train: 1, Test: 0, Episode: 579, fc1_units: 48, fc2_units: 32, eps_start: 0.992, Score: 15.0\n",
      "Train: 1, Test: 1, Episode: 579, fc1_units: 48, fc2_units: 32, eps_start: 0.992, Score: 9.0\n",
      "Train: 1, Test: 2, Episode: 579, fc1_units: 48, fc2_units: 32, eps_start: 0.992, Score: 17.0\n",
      "Train: 1, Test: 3, Episode: 579, fc1_units: 48, fc2_units: 32, eps_start: 0.992, Score: 17.0\n",
      "Train: 1, Test: 4, Episode: 579, fc1_units: 48, fc2_units: 32, eps_start: 0.992, Score: 18.0\n",
      "Train: 1, Test: 5, Episode: 579, fc1_units: 48, fc2_units: 32, eps_start: 0.992, Score: 17.0\n",
      "       Average Score:  15.5\n",
      "=========================================================\n",
      "Train: 2, Test: 0, Episode: 572, fc1_units: 80, fc2_units: 88, eps_start: 0.992, Score: 18.0\n",
      "Train: 2, Test: 1, Episode: 572, fc1_units: 80, fc2_units: 88, eps_start: 0.992, Score: 11.0\n",
      "Train: 2, Test: 2, Episode: 572, fc1_units: 80, fc2_units: 88, eps_start: 0.992, Score: 12.0\n",
      "Train: 2, Test: 3, Episode: 572, fc1_units: 80, fc2_units: 88, eps_start: 0.992, Score: 10.0\n",
      "Train: 2, Test: 4, Episode: 572, fc1_units: 80, fc2_units: 88, eps_start: 0.992, Score: 13.0\n",
      "Train: 2, Test: 5, Episode: 572, fc1_units: 80, fc2_units: 88, eps_start: 0.992, Score: 13.0\n",
      "       Average Score:  12.8333333333\n",
      "=========================================================\n",
      "Train: 3, Test: 0, Episode: 590, fc1_units: 64, fc2_units: 56, eps_start: 0.994, Score: 13.0\n",
      "Train: 3, Test: 1, Episode: 590, fc1_units: 64, fc2_units: 56, eps_start: 0.994, Score: 20.0\n",
      "Train: 3, Test: 2, Episode: 590, fc1_units: 64, fc2_units: 56, eps_start: 0.994, Score: 19.0\n",
      "Train: 3, Test: 3, Episode: 590, fc1_units: 64, fc2_units: 56, eps_start: 0.994, Score: 16.0\n",
      "Train: 3, Test: 4, Episode: 590, fc1_units: 64, fc2_units: 56, eps_start: 0.994, Score: 18.0\n",
      "Train: 3, Test: 5, Episode: 590, fc1_units: 64, fc2_units: 56, eps_start: 0.994, Score: 14.0\n",
      "       Average Score:  16.6666666667\n",
      "=========================================================\n",
      "Train: 4, Test: 0, Episode: 633, fc1_units: 80, fc2_units: 88, eps_start: 0.989, Score: 16.0\n",
      "Train: 4, Test: 1, Episode: 633, fc1_units: 80, fc2_units: 88, eps_start: 0.989, Score: 15.0\n",
      "Train: 4, Test: 2, Episode: 633, fc1_units: 80, fc2_units: 88, eps_start: 0.989, Score: 16.0\n",
      "Train: 4, Test: 3, Episode: 633, fc1_units: 80, fc2_units: 88, eps_start: 0.989, Score: 14.0\n",
      "Train: 4, Test: 4, Episode: 633, fc1_units: 80, fc2_units: 88, eps_start: 0.989, Score: 16.0\n",
      "Train: 4, Test: 5, Episode: 633, fc1_units: 80, fc2_units: 88, eps_start: 0.989, Score: 12.0\n",
      "       Average Score:  14.8333333333\n",
      "=========================================================\n"
     ]
    }
   ],
   "source": [
    "def checkWeights(env, train_n, test, fc1_n, fc2_n, eps_s, episodes):\n",
    "    agent = Agent(state_size=37, action_size=4, seed=17, fc1_units=fc1_n, fc2_units=fc2_n)  \n",
    "    file_weights = 'weights_'+str(train_n)+'.trn'\n",
    "    agent.qnetwork_local.load_state_dict(torch.load(file_weights))\n",
    "\n",
    "    env_info = env.reset(train_mode=False)[brain_name] # reset the environment\n",
    "    state = env_info.vector_observations[0]            # get the current state\n",
    "    score = 0                                          # initialize the score\n",
    "    while True:\n",
    "        action = agent.act(state,.05)                  # select an action\n",
    "        action = int(action)\n",
    "        env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "        next_state = env_info.vector_observations[0]   # get the next state\n",
    "        reward = env_info.rewards[0]                   # get the reward\n",
    "        done = env_info.local_done[0]                  # see if episode has finished\n",
    "        score += reward                                # update the score\n",
    "        state = next_state                             # roll over the state to next time step\n",
    "        if done:                                       # exit loop if episode finished\n",
    "            break\n",
    "    \n",
    "    print('Train: {}, Test: {}, Episode: {}, fc1_units: {}, fc2_units: {}, eps_start: {}, Score: {}'\\\n",
    "          .format(train_n, test, episodes, fc1_n, fc2_n, eps_s, score))\n",
    "    return score\n",
    "\n",
    "for i in range(0, numb_of_trains):\n",
    "    fc1_nodes = list_fc1_units[i]\n",
    "    fc2_nodes = list_fc2_units[i]\n",
    "    eps_start = list_eps_start[i]\n",
    "    episodes  = list_episodes[i]\n",
    "    list_scores = []\n",
    "    for test in range(0,6):        \n",
    "        score = checkWeights(env=env, train_n=i, test=test, fc1_n=fc1_nodes, fc2_n=fc2_nodes, eps_s=eps_start,episodes=episodes)\n",
    "        list_scores.append(score)\n",
    "    avg_score =  np.mean(list_scores)\n",
    "    print('       Average Score: ', avg_score)\n",
    "    print('=========================================================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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